A comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration

Abstract

The present invention discloses a comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration, specifically: first construct a multi-source spatiotemporal data set including remote sensing images, open source map services, social media information, geographic spatial information and statistical data to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity; then based on the multi-source spatiotemporal data set, the potential and actual resource and environmental carrying capacity indexes are calculated using grids and administrative divisions as evaluation units; finally, the spatial characteristics of resource and environmental carrying capacity are analyzed using spatial autocorrelation and hot spot analysis methods, and the resource and environmental carrying capacity evaluation result map and its spatial characteristic distribution map are drawn. The present invention can effectively make up for the lack of accuracy and timeliness of traditional data and methods in the evaluation of urban resource and environmental carrying capacity, and has stronger applicability to the comprehensive evaluation of urban resource and environmental carrying capacity, and can accurately identify the potential and actual spatial distribution characteristics of urban resource and environmental carrying capacity.

Description

Technical Field

[0001] The present invention belongs to the field of natural resource management, and in particular relates to a comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration. Background Art

[0002] Resource and environmental carrying capacity refers to the maximum limit of human development and utilization activities that can be carried by resource, environment, society, and economy subsystems within a certain geographical space. As a complex human-land coupling system, the resource, environment, ecology, and social structure subsystems of a city are highly intertwined, and the resource and environmental carrying capacity of a city usually has significant systematic, complex, and dynamic characteristics. The contradiction between the limited supply of materials in the urban resource and environmental system and the unlimited development and utilization needs of humans is becoming increasingly prominent. However, due to the lack of in-depth understanding of the urban resource, environment, ecology, society, and economy subsystems, it is difficult for relevant urban decision-making agencies to comprehensively consider the complexity of the urban system when formulating development outlines and development and utilization plans. The accompanying series of typical urban resource and environmental problems such as resource depletion, environmental degradation, ecological destruction, over-exploitation of energy, and population congestion are increasingly becoming bottlenecks that restrict the sustainable development of cities and the healthy lives of urban residents. Therefore, it is urgent to use a comprehensive method to conduct a comprehensive and in-depth evaluation of the urban resource and environmental carrying capacity, so as to accurately understand the potential and actual carrying capacity of urban resources, environment, ecology, and socio-economic systems, and provide a more scientific reference for relevant departments to make decisions on optimizing the urban land space structure.

[0003] In the evaluation of urban resource and environmental carrying capacity, domestic and foreign scholars have contributed a large number of theoretical and technical paradigms, mainly focusing on resource and environmental carrying capacity evaluation data and resource and environmental carrying capacity evaluation method systems. On the one hand, in terms of resource and environmental carrying capacity evaluation data, some studies take the connotation of resource and environmental carrying capacity in a certain field as the starting point, and choose to use the geographic spatial data or statistical data corresponding to the resource and environmental carrying capacity in that field for evaluation. However, the data used in the carrying capacity evaluation often have the common problem of being outdated and lacking in timeliness; on the other hand, in terms of the resource and environmental carrying capacity evaluation method system, some studies have evaluated the carrying capacity of ecological carrying capacity, population carrying capacity, land carrying capacity, and water resources carrying capacity. However, in the new context, the carrying capacity evaluation results obtained by these studies have the defect of insufficient accuracy. Some cases are as follows:

[0004] From the perspective of data timeliness, Tian Peng evaluated the ecological carrying capacity of China's uninhabited islands based on ecological monitoring data such as soil composition, precipitation, and vegetation type; Tao Weiliang evaluated the land resource carrying capacity of Anhui Province, China based on land use data, population data, and national economic output data; Ding Xiangyi evaluated the water resource carrying capacity of Taiyuan City, Shanxi Province, China based on water quality, water quantity, and population data. By using geospatial data such as soil, land use, and vegetation, as well as statistical data including population and socio-economics, resource and environmental carrying capacity evaluation studies in different fields have been carried out. Although these methods have derived a data usage paradigm for resource and environmental carrying capacity evaluation, in the new context, these evaluation data often have the common problem of being outdated and lacking timeliness, resulting in the resource and environmental carrying capacity evaluation results often lagging behind the actual situation. The Technical Guidelines for the Evaluation of Resource and Environmental Carrying Capacity and Land and Space Development Suitability (Trial Version) provides detailed references for the selection of indicators and data application for resource and environmental carrying capacity evaluation, and has achieved remarkable results. At the same time, it also proposes the concept of integrating time and space data from different sources and of various types and applying them to resource and environmental carrying capacity evaluation. However, due to the limitations of multi-source spatiotemporal big data acquisition technology, no scholar has put this concept into practice, and a mature and operational multi-source spatiotemporal data integration model has not yet been formed. For example, Wang Jing evaluated the resource and environmental carrying capacity of Yantai City, China by using the reference evaluation data indicators provided by this guide, and obtained the evaluation results of Yantai City's resource and environmental carrying capacity. However, since the evaluation data used are historical static geographic spatial information data and statistical data, it has not been able to deeply explore the deep-level characteristics of the resource and environmental carrying capacity system under the current state of the city, and it is difficult to provide help for the refined needs of urban land and space planning in the new era.

[0005] From the perspective of the accuracy of the resource and environmental carrying capacity evaluation method system, Hawden and Palmert monitored the changes in the number of reindeer populations in Alaska, USA, and analyzed the ecological carrying capacity under specific habitat conditions based on the relationship between regional area and organisms. Wang Guoqiang proposed an evaluation index system for resource and environmental carrying capacity in ecological regions, and obtained a paradigm for the evaluation index system of ecological carrying capacity in specific regions. Aldo Leopold analyzed the population carrying capacity by calculating the maximum population that can be accommodated per unit area of ​​space without considering food support. William Allan took African agriculture and animal husbandry as the research object and calculated the maximum number of people that can be carried on a certain unit area of ​​land while maintaining a certain type of land use. Shi Yafeng calculated the water resource carrying capacity of the Urumqi River Basin in Xinjiang, China in his monograph "Water Resource Carrying Capacity and Rational Utilization of the Urumqi River Basin". The above studies calculated the resource and environmental carrying capacity in different fields such as ecological carrying capacity, population carrying capacity, and water resource carrying capacity, and obtained rich examples of regional resource and environmental carrying capacity evaluation method systems. However, these studies all place the resource subsystem, environmental subsystem, ecological subsystem and socio-economic subsystem of the city in a unified system to carry out resource and environmental carrying capacity assessment, which can only produce single-level resource and environmental carrying capacity assessment results, and fail to deeply characterize the potential implicit state and actual representation state of the urban resource and environmental system. In the context of the rapid update of the current urban resource and environmental system elements, the accuracy of this single urban resource and environmental carrying capacity assessment result can no longer be guaranteed, making it difficult for the resource and environmental carrying capacity assessment results to scientifically meet the precise needs of sustainable development of urban resources and environment.

[0006] Based on the above background analysis, it can be seen that with the acceleration of urbanization, the complexity of urban resource and environmental systems has become more and more significant, and the problem of resource and environmental overload caused by unreasonable development and utilization activities has become increasingly severe. It is imperative to conduct a comprehensive, comprehensive and in-depth evaluation of urban resource and environmental carrying capacity. However, relying solely on traditional geographic spatial data and statistical data cannot deeply explore the dynamic characteristics of urban resources, environment, ecology and socio-economic systems, and it is difficult to accurately identify the potential and actual state of urban resource and environmental carrying capacity, which leads to different degrees of disconnection between the evaluation results of resource and environmental carrying capacity and the actual situation, and it is difficult to provide a scientific reference for the optimization of urban spatial land layout in the new era.

[0007] The comprehensive evaluation method of urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration proposed in the present invention significantly makes up for the lack of data timeliness of traditional methods in the comprehensive evaluation of urban resource and environmental carrying capacity by introducing remote sensing images, open source map services, social media information, geographic spatial information and statistical data. The present invention integrates the urban resource and environmental carrying system into two dimensions, potential and reality, to carry out carrying capacity evaluation, effectively improving the lack of accuracy of the traditional urban resource and environmental carrying capacity evaluation method system. It has stronger applicability to the comprehensive evaluation of urban resource and environmental carrying capacity, and can accurately identify the potential and actual resource and environmental carrying capacity of the city and its comprehensive spatial distribution characteristics. More importantly, the present method is suitable for the in-depth characterization of the comprehensive carrying status of urban resources, environment, ecology and socio-economic systems, and can achieve a comprehensive, comprehensive and in-depth evaluation of urban resource and environmental carrying capacity, which has great practical significance for expanding the application field of geographic spatiotemporal big data and urban land space planning. Summary of the invention

[0008] The technical problem to be solved by the present invention is: in order to deeply explore the comprehensive resource and environmental carrying capacity of a city, a comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration is provided, which can effectively identify the comprehensive carrying capacity and spatial distribution characteristics of the urban resource and environmental system from the two dimensions of the potential state and the actual state of the urban resource and environmental carrying system, and is a beneficial supplement to the traditional urban resource and environmental carrying capacity evaluation method.

[0009] The present invention solves the technical problem by adopting the following technical solutions:

[0010] The urban resource and environmental carrying capacity evaluation method based on multi-source spatiotemporal data integration provided by the present invention is characterized by: firstly constructing a multi-source spatiotemporal data set including remote sensing images, open source map services, social media information, geographic spatial information and statistical data to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity; then, based on the multi-source spatiotemporal data set, the potential and actual resource and environmental carrying capacity indexes are calculated using grids and administrative divisions as evaluation units respectively; finally, the spatial characteristics of resource and environmental carrying capacity are analyzed using spatial autocorrelation and hot spot analysis methods, and a resource and environmental carrying capacity evaluation result map and its spatial characteristic distribution map are drawn.

[0011] The present invention can construct the multi-source spatiotemporal dataset by the following method:

[0012] (1) Preprocessing of remote sensing image data including land use classification, digital elevation, normalized difference vegetation index, and night light brightness by projection transformation, data clipping, and mosaicking;

[0013] (2) Write Python language programs to call online open source map service interfaces to obtain geographic points of interest (POIs), traffic network, and social media information data, and perform data cleaning and data spatialization preprocessing;

[0014] (4) Preprocessing of geospatial information data including soil organic matter content, annual average precipitation, annual average solar radiation, and geological stability by projection transformation, data clipping, and data mosaicking;

[0015] (5) Preprocessing of data cleaning and data spatialization steps for statistical data from statistical yearbooks, health statistics yearbooks, and water resources bulletins;

[0016] (6) The pre-processed land use classification, digital elevation, normalized difference vegetation index, night light brightness, geographic points of interest (POI), traffic network, social media entries, soil organic matter content, average annual precipitation, average annual solar radiation, geological stability, population, GDP, water resource utilization rate, and sewage treatment rate data were spatially corrected and then clipped according to the boundary shape of the study area to make the dataset consistent with the spatial range of the study area, thus generating a multi-source spatiotemporal dataset for the comprehensive evaluation of urban resource and environmental carrying capacity in the study area.

[0017] The present invention can use the following method to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity:

[0018] Focusing on the characteristics and current status of the components of the urban resource subsystem, environmental subsystem, social and economic subsystem, the resource and environmental carrying system is integrated into two dimensions: potential resource and environmental carrying capacity and actual resource and environmental carrying capacity. The analytic hierarchy process is used to construct the urban potential and actual resource and environmental carrying capacity evaluation index systems. Each system consists of a target layer, a criterion layer and an indicator layer. The target layer is the potential resource and environmental carrying capacity and the actual resource and environmental carrying capacity. The target layer includes a criterion layer classified into multiple resource and environmental subsystems, and the criterion layer includes an indicator layer containing several indicators.

[0019] The criterion layer of the potential resource and environmental carrying capacity evaluation index system includes three resource and environmental subsystems: ecosystem service function, soil and water conservation function, and ecosystem sensitivity; the indicator layer of the potential resource and environmental carrying capacity evaluation index system includes water conservation capacity, wind and sand fixation capacity, flood storage capacity, soil water retention, soil organic matter content, vegetation index NDVI, soil erosion sensitivity, and acid rain sensitivity, a total of 8 indicators;

[0020] The criterion layer of the actual resource and environmental carrying capacity evaluation index system includes five resource and environmental subsystems: resource endowment limitation, environmental quality safety, urban development resilience, infrastructure perfection, and social development intensity; the indicator layer of the actual resource and environmental carrying capacity evaluation index system includes per capita cultivated land area, terrain undulation, per capita water resources, geological disaster risk, flood disaster risk, PM2.5 concentration, distribution density of enterprises and institutions, proportion of the tertiary industry, distribution density of scientific and educational resources, accessibility of public service facilities, population density, and transportation network density, totaling 12 indicators.

[0021] The present invention can use the following method to calculate the indicators of each criterion layer in the resource and environmental carrying capacity evaluation index system:

[0022] (1) Calculation method of the criterion level indicators in the potential resource and environmental carrying capacity evaluation index system:

[0023] a. The calculation method of ecosystem service function is shown in Formula 1:

[0024] ES=Wr×w1+Ws×w2+Fs×w3 Formula 1

[0025] In the formula, ES refers to the ecosystem service function index; Wr refers to the water conservation capacity, Ws refers to the wind and sand fixation capacity, and Fs refers to the flood storage capacity; w i Refers to the weight value of the indicator;

[0026] b. The calculation method of soil and water conservation function is shown in Formula 2:

[0027] WSS=Ss×w1+So×w2+NDVI×w3 Formula 2

[0028] Where, WSS refers to the soil and water conservation function index, Ss refers to soil water retention, So refers to soil organic matter content, and NDVI refers to vegetation coverage index; i Refers to the weight value of the indicator;

[0029] c. The calculation method of ecosystem sensitivity is shown in Formula 3:

[0030] ER=Se×w1+As×w2 Formula 3

[0031] Where ER refers to the ecosystem sensitivity index; Se refers to soil erosion sensitivity; As refers to acid rain sensitivity; w i Refers to the weight value of the indicator;

[0032] (2) Calculation method of criterion-level indicators in the current resource and environmental carrying capacity evaluation index system:

[0033] d. The calculation method of resource endowment restriction is shown in Formula 4:

[0034] RR=fs / pop×w1+dem×w2+pw×w3 Formula 4

[0035] In the formula, RR refers to the resource endowment restriction index; fs refers to the cultivated land area; pop refers to the population; dem refers to the terrain relief; pw refers to the per capita water resources; w i Refers to the weight value of the indicator;

[0036] e. The calculation method of environmental quality safety is shown in Formula 5:

[0037] EQS=dr×w1+fr×w2+aq×w3 Formula 5

[0038] In the formula, EQS refers to the environmental quality safety index; dr refers to geological stability; fr refers to flood disaster risk, and the flood disaster risk value is obtained through Python language program; aq refers to the annual average PM2.5 concentration; w i Refers to the weight value of the indicator;

[0039] f. The calculation method of urban development resilience is shown in Formula 6:

[0040] CR = cn / s×w1+ps / gdp×w2+sn / s×w3 Formula 6

[0041] In the formula, CR refers to the urban development resilience index; cn refers to the number of enterprises and institutions, ps refers to the output value of the tertiary industry, gdp refers to the gross national product of the evaluation unit, sn refers to the distribution density of scientific and educational resources, and s refers to the area of ​​the evaluation unit; w i Refers to the weight value of the indicator;

[0042] g. The calculation method of infrastructure completeness is as follows:

[0043] Set a grid size of 2000 meters, take the geometric center point of each grid as the demand point, and the location of various infrastructure as the supply point. By writing a Python language program, call the Amap route planning service API to obtain the shortest travel time between each demand point and its nearest supply point. The specific calculation method is shown in Formula 7 and Formula 8:

[0044] T i =Min(t i1 , t i2 , t i3 ,...t ij ) Formula 7

[0045]

[0046] Where, T i Refers to the shortest travel time of sampling point i, in seconds, tij It refers to the optimal travel time required from demand point i to one of the target points of a certain type of infrastructure, j refers to the number of a certain type of infrastructure within 10km of the sampling point; IP refers to the infrastructure completeness index, s i Refers to the shortest travel time between the demand point and its nearest supply point, and n refers to the number of sampling points in the evaluation unit. In order to be consistent with other indicators, after calculating the infrastructure perfection index, the grid-scale infrastructure perfection index is converted to the administrative division scale through the area-weighted average method;

[0047] h. The calculation method of social development intensity is shown in Formula 9:

[0048] SD=pop / s×w1+rl×w2 Formula 9

[0049] In the formula, SD refers to the social development intensity index; pop refers to the total population in the evaluation unit; s refers to the area of ​​the evaluation unit; rl refers to the length of the traffic network in the evaluation unit; w i Refers to the weight value of this indicator.

[0050] The present invention can use the entropy weight method to calculate the weight of each evaluation index, and the specific steps are as follows:

[0051] (1) Under the same indicator, the proportion of the value of the jth evaluation indicator in the ith evaluation unit to the sum of the values ​​of the indicator in all evaluation units is P ij , expressed by formula 10:

[0052]

[0053] In the formula, F ij Refers to the value of the jth indicator in the i-th evaluation unit;

[0054] e j Refers to the entropy value of each indicator, calculated using formula 11:

[0055]

[0056] In the formula, k is a constant, k = l / ln m, m is the number of samples, 0 < e j <1;

[0057] The weight of each evaluation index is calculated using formula 12:

[0058]

[0059] In the formula, w j Refers to the weight value of each indicator.

[0060] The present invention can use the range standardization method to standardize each index, and use formula 13 and formula 14 to calculate:

[0061] (1) For positive indicators, the standardization method is:

[0062] r ij =[v ij -min(v ij )] / [max(v ij )-min(v ij )] Formula 13

[0063] (2) For negatively pointing indicators, the standardization method is:

[0064] r ij =[max(v ij )-v ij ] / [max(v ij )-min(v ij )] Formula 14

[0065] In the formula, r ij Refers to the index value after standardization; v ij Refers to the original value of the indicator data; max(v ij ) refers to the maximum value of the index; min(v ij ) means the minimum value of that indicator.

[0066] In the method of the present invention, the grid is used as the basic unit for evaluating the potential resource and environmental carrying capacity, and the administrative division boundary is used as the basic unit for evaluating the actual resource and environmental carrying capacity;

[0067] The urban resource and environmental carrying capacity index is comprehensively calculated using Formula 15 and Formula 16:

[0068]

[0069] In the formula, RECC p Refers to the potential resource and environmental carrying capacity index, RECC r It refers to the actual resource and environmental carrying capacity index, ES refers to ecosystem service function, WSS refers to ecosystem risk, ER refers to habitat quality; RR refers to resource endowment limitation, EQS refers to environmental quality safety, CR refers to urban development resilience, IP refers to infrastructure perfection, and SD refers to social development intensity.

[0070] The present invention can use the following method to calculate the spatial distribution characteristics of urban resource and environmental carrying capacity:

[0071] (1) In each basic evaluation unit, the global Moran's I of the actual resource and environmental carrying capacity index is calculated using formula 17;

[0072]

[0073] In the formula, I refers to the global Moran's I, n refers to the number of resource and environmental carrying capacity assessment units, and X i and X j Refers to the resource and environmental carrying capacity index value, w ij Refers to the spatial weight matrix between each evaluation unit;

[0074] Using the hot spot analysis method, the spatial aggregation characteristics of the resource and environmental carrying capacity index of the study area were calculated using Formula 18:

[0075]

[0076] In the formula, refers to the clustering index, and S refers to the standard deviation;

[0077] (2) The Moran index and clustering index of the potential and actual resource and environmental carrying capacity index of the study area were spatially mapped to obtain the results of the spatial characteristics analysis of the resource and environmental carrying capacity of the study area.

[0078] The present invention can use the following method to visualize the resource and environmental carrying capacity assessment results: using ArcMap 10.8 software, according to the natural breakpoint method, the urban resource and environmental carrying capacity assessment results and the resource and environmental carrying capacity spatial characteristic analysis results of the study area are hierarchically displayed on the map, and a comprehensive assessment result map of the urban resource and environmental carrying capacity of the study area and a spatial characteristic analysis result map are generated respectively.

[0079] The method described in the present invention is used to deeply explore the comprehensive status of the resource and environmental carrying capacity of a city.

[0080] Compared with the prior art, the present invention has the following main technical achievements:

[0081] (1) By integrating remote sensing images, open source map services, social media information, geographic monitoring and statistical data, an integrated fusion analysis of multi-source heterogeneous spatiotemporal data is achieved, which provides a larger and more timely data source for the comprehensive evaluation of urban resource and environmental carrying capacity, effectively making up for the lack of accuracy and timeliness of single-type traditional data and methods in the comprehensive evaluation of urban resource and environmental carrying capacity, and greatly improving the comprehensiveness and scientificity of urban resource and environmental carrying capacity evaluation.

[0082] (2) Based on the current characteristics of urban resource systems, environmental systems, ecological systems, and socio-economic systems, a comprehensive evaluation index system for urban resource and environmental carrying capacity was constructed based on the two scenarios of potential resource and environmental carrying capacity and actual resource and environmental carrying capacity. The urban resource and environmental carrying capacity was comprehensively evaluated using two basic evaluation units, namely grid and administrative division. The spatial econometric model of spatial autocorrelation was used to analyze the spatial distribution characteristics of resource and environmental carrying capacity. This technical system is more applicable to the comprehensive evaluation of urban resource and environmental carrying capacity, and can comprehensively and accurately identify the comprehensive characteristics of urban resource and environmental carrying capacity, providing a scientific and reasonable reference basis for urban natural resource management departments to formulate urban land space planning and decision-making.

[0083] In summary, the comprehensive evaluation method of urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration proposed in the present invention provides a method for integrating spatiotemporal data from multiple sources and of different types, which effectively makes up for the defects of low data accuracy and lack of timeliness in traditional urban resource and environmental carrying capacity evaluation; more importantly, this method integrates urban resource and environmental carrying capacity into two dimensions: potential resource and environmental carrying capacity and actual resource and environmental carrying capacity, which can effectively identify the comprehensive carrying capacity of the urban resource and environmental system and its spatial distribution characteristics. This method is suitable for urban resource and environmental evaluation in the new era, and can realize a profound and comprehensive test of urban resource and environmental carrying capacity. It is a beneficial supplement to the urban resource and environmental carrying capacity evaluation method, and is of great significance to expanding the application field of resource and environmental analysis and evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 The present invention is a flow chart of the method.

[0085] Figure 2 Integrate relationship diagram for multi-source spatiotemporal data.

[0086] Figure 3 Figure 1 is the result of the resource and environmental carrying capacity assessment in the research area, where: Figure (a) is the result of the potential resource and environmental carrying capacity assessment, and Figure (b) is the result of the actual resource and environmental carrying capacity assessment.

[0087] Figure 4 This is the result map of the spatial characteristics distribution of regional resource and environmental carrying capacity, where: Figure (a) is the spatial characteristics distribution map of potential resource and environmental carrying capacity, and Figure (b) is the spatial characteristics distribution map of actual resource and environmental carrying capacity. DETAILED DESCRIPTION

[0088] The present invention discloses a comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration, the method comprising the following steps: S1, constructing a multi-source spatiotemporal data set integrating remote sensing images, open source map services, social media information, geospatial information and statistical data; S2, determining a comprehensive evaluation index system for urban resource and environmental carrying capacity; S3, calculating potential and actual resource and environmental carrying capacity indexes using grids and administrative divisions as evaluation units respectively; S4, using spatial autocorrelation and hot spot analysis methods to analyze the spatial characteristics of resource and environmental carrying capacity, and drawing a resource and environmental carrying capacity evaluation result map and its spatial characteristic distribution map. The present invention can effectively make up for the lack of accuracy and timeliness of traditional data and methods in the evaluation of urban resource and environmental carrying capacity, has stronger applicability to the comprehensive evaluation of urban resource and environmental carrying capacity, and can accurately identify the potential and actual spatial distribution characteristics of urban resource and environmental carrying capacity.

[0089] The present invention is further described below in conjunction with application examples and drawings, but is not limited to the following contents.

[0090] The method for comprehensive evaluation of urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration provided by the present invention is specifically as follows: first, a comprehensive data set integrating remote sensing images, open source map services, social media information, geographic spatial information and statistics is constructed, and then a comprehensive evaluation index system for urban resource and environmental carrying capacity is determined; secondly, based on the multi-source spatiotemporal data set, grids and administrative divisions are used as basic evaluation units to calculate potential and actual resource and environmental carrying capacity indexes; finally, spatial autocorrelation and hot spot analysis methods are used to analyze the spatial characteristics of the resource and environmental carrying capacity index, and the resource and environmental carrying capacity evaluation results and their spatial characteristic analysis results are visualized to draw a resource and environmental carrying capacity evaluation result map and a carrying capacity spatial characteristic distribution map.

[0091] The above-mentioned comprehensive evaluation method of urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration includes the following steps:

[0092] (1) Preprocessing of remote sensing image data including land use classification, digital elevation, normalized difference vegetation index, and night light brightness by three steps: projection transformation, data clipping, and mosaicking;

[0093] (2) Write a Python program to call the open source service interface of the online map to obtain geographic points of interest (POIs), traffic network data, and social media information entry data, and perform data cleaning and data spatial preprocessing;

[0094] (3) Visit the official websites of the National Earth Science System Data Service Center and the National Meteorological Science Data Center to apply for downloading soil organic matter content data, annual average precipitation data, annual average solar radiation data, and geological stability data, and perform three steps of preprocessing on these geographic spatial information data: projection transformation, data clipping, and mosaicking;

[0095] (4) By visiting the official websites of the Statistics Bureau, Health Commission, and Water Affairs Bureau, apply to download the statistical yearbook, health statistics yearbook, and water resources bulletin data of the study area, and perform data cleaning and spatial preprocessing on these statistical data to obtain statistical data on population, GDP, water resource utilization rate, and sewage treatment rate in the study area;

[0096] (5) The above pre-processed remote sensing image data including land use classification, digital elevation, normalized difference vegetation index and night light, news media information data including geographic points of interest (POI), transportation network, social media entries, geospatial data including soil organic matter content, average annual precipitation, average annual solar radiation, geological stability, and statistical data including population, GDP, water resource utilization rate, and sewage treatment rate were all spatially corrected and then clipped according to the boundary shape of the study area to make the dataset consistent with the spatial range of the study area, thus generating a multi-source spatiotemporal dataset for the comprehensive evaluation of urban resource and environmental carrying capacity in the study area.

[0097] Among the above methods, the following methods can be used to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity:

[0098] Focusing on the characteristics and current status of the elements of the urban resource system, environmental system, social and economic system, the resource and environmental carrying system is integrated into two dimensions: potential resource and environmental carrying capacity and actual resource and environmental carrying capacity. The hierarchical analysis method is used to construct the evaluation index system of urban potential and actual resource and environmental carrying capacity. The two systems are composed of the target layer, the criterion layer and the indicator layer. The target layer is the potential resource and environmental carrying capacity and the actual resource and environmental carrying capacity; the criterion layer of the potential resource and environmental carrying capacity evaluation index system includes three indicators: ecosystem service function, soil and water conservation function, and ecosystem sensitivity; the criterion layer of the actual resource and environmental carrying capacity evaluation index system includes five indicators: resource endowment limitation, environmental quality safety, urban development resilience, infrastructure perfection, and social development intensity; the potential resource and environmental carrying capacity indicator layer includes 8 indicators, and the actual resource and environmental carrying capacity indicator layer includes 12 indicators.

[0099] Among the above methods, the following method can be used to calculate the urban resource and environmental carrying capacity index:

[0100] First, the entropy weight method is used to calculate the weight of each indicator. The specific steps are as follows:

[0101] (1) Under the same indicator, calculate the proportion of the value of the jth evaluation indicator in the ith evaluation unit to the sum of the values ​​of the indicator in all evaluation units, as shown in Formula 1:

[0102]

[0103] Where P ij It refers to the proportion of the value of the jth evaluation index in the ith evaluation unit to the sum of the values ​​of the index in all evaluation units. ij It refers to the value of the jth indicator in the ith evaluation unit, and n refers to the number of samples.

[0104] (2) Calculate the entropy value of each indicator. The specific method is shown in Formula 2:

[0105]

[0106] In the formula, e j Refers to the entropy value of each indicator, k refers to a constant, k = 1 / ln m, m refers to the number of samples, 0 < e j <1; P ij It refers to the proportion of the value of the jth evaluation indicator in the ith evaluation unit to the sum of the values ​​of the indicator in all evaluation units.

[0107] The calculation method of each indicator weight is shown in Formula 3:

[0108]

[0109] In the formula, w j Refers to the weight value of each indicator, e j Refers to the entropy value of each indicator.

[0110] Secondly, the range standardization method is used to standardize the calculated values ​​of each indicator, as shown in Formula 4 and Formula 5:

[0111] a. For positive pointing indicators, the standardization method is:

[0112] r ij =[v ij -min(v ij )] / [max(v ij )-min(v ij )] Formula 4

[0113] b. For negatively pointing indicators, the standardization method is:

[0114] r ij =[max(v ij )-v ij ] / [max(vij )-min(v ij )] Formula 5

[0115] In the formula, r ij Refers to the index value after standardization; v ij Refers to the original value of the indicator data; max(v ij ) refers to the maximum value of the index; min(v ij ) means the minimum value of that indicator.

[0116] Finally, the resource and environmental carrying capacity index is calculated according to the following method:

[0117] 1. The calculation methods of indicators at each criterion level in the potential resource and environmental carrying capacity evaluation index system are as follows:

[0118] a. The calculation method of ecosystem service function indicators is shown in Formula 6:

[0119] ES=Wr×w1+Ws×w2+Fs×w3 Formula 6

[0120] In the formula, ES refers to the ecosystem service function index; Wr refers to water conservation capacity, Ws refers to wind and sand fixation capacity, and Fs refers to flood storage capacity; w1, w2, and w3 are the weight values ​​of water conservation capacity, wind and sand fixation capacity, and flood storage capacity, respectively. b. The calculation method of soil and water conservation function index is shown in Formula 7:

[0121] WSS=Ss×w1+So×w2+NDVI×w3 Formula 7

[0122] Wherein, WSS refers to the soil and water conservation function index, Ss refers to soil water retention, So refers to soil organic matter content, and NDVI refers to vegetation coverage index; w1, w2, and w3 are the weight values ​​of soil water retention, soil organic matter content, and vegetation coverage index NDVI, respectively.

[0123] c. The calculation method of ecosystem sensitivity index is shown in Formula 8:

[0124] ER=Se×w1+As×w2 Formula 8

[0125] Where ER refers to the ecosystem sensitivity index; Se refers to soil erosion sensitivity, As refers to acid rain sensitivity; w1 and w2 are the weight values ​​of soil erosion sensitivity and ecological sensitivity, respectively.

[0126] (2) The calculation methods of the indicators at each intermediate level of the current resource and environmental carrying capacity evaluation index system are as follows:

[0127] d. The calculation method of resource endowment restriction index is shown in Formula 9:

[0128] RR=fs / pop×w1+dem×w2+pw×w3 Formula 9

[0129] Wherein, RR refers to the resource endowment restriction index; fs refers to the cultivated land area; pop refers to the population; dem refers to the terrain relief; pw refers to the per capita water resources; w1, w2, and w3 are the weight values ​​of per capita cultivated land area, terrain relief, and per capita water resources, respectively.

[0130] e. The calculation method of environmental quality safety index is shown in formula 10:

[0131] EQS=dr×w1+fr×w2+aq×w3 Formula 10

[0132] In the formula, EQS refers to the environmental quality safety index; dr refers to geological stability; fr refers to flood disaster risk, and the flood disaster risk value is obtained through Python language program; aq refers to the average annual PM2.5 concentration; w1, w2, and w3 are the weight values ​​of geological stability, flood disaster risk, and average annual PM2.5 concentration, respectively.

[0133] f. The calculation method of urban development resilience index is shown in Formula 11:

[0134] CR = cn / s×w1+ps / gdp×w2+sn / s×w3 Formula 11

[0135] In the formula, CR refers to the urban development resilience index; cn refers to the number of enterprises and institutions, ps refers to the output value of the tertiary industry, gdp refers to the gross national product of the evaluation unit, sn refers to the number of schools and scientific research institutions, and s refers to the area of ​​the evaluation unit; w1, w2, and w3 are the weight values ​​of the distribution density of enterprises and institutions, the proportion of the tertiary industry, and the distribution density of scientific and educational resources, respectively.

[0136] g. The calculation method of infrastructure integrity index is as follows:

[0137] Set a grid size of 2000 meters, take the geometric center point of each grid as the demand point, and the location of various infrastructure as the supply point. By writing a Python language program, call the Amap route planning service API interface to obtain the shortest travel time between each demand point and its nearest supply point. The specific calculation method is shown in Formula 12 and Formula 13:

[0138] T i =Min(t i1 , t i2 , t i3 ,...t ij ) Formula 12

[0139]

[0140] In Formula 13 and Formula 14: T i Refers to the shortest travel time of sampling point i, in seconds; t ij It refers to the optimal travel time required from demand point i to one of the target points of a certain type of infrastructure, in seconds; j refers to the number of a certain type of infrastructure within 10km of the sampling point. IP refers to the infrastructure integrity index; s i Refers to the shortest travel time between the demand point and its nearest supply point, and n refers to the number of sampling points in the evaluation unit. In order to be consistent with other indicators, after calculating the infrastructure perfection index, the grid-scale infrastructure perfection index is converted to the administrative division scale through the area-weighted average method.

[0141] h. The calculation method of social development intensity index is shown in formula 14:

[0142] SD=pop / s×w1+rl / s×w2 Formula 14

[0143] In the formula, SD refers to the social development intensity index; pop refers to the total population in the evaluation unit, s refers to the area of ​​the evaluation unit; rl refers to the length of the traffic network in the evaluation unit; w1 and w2 are the weight values ​​of population density and traffic network density, respectively.

[0144] Taking the street-level administrative division boundary as the basic unit for resource and environmental carrying capacity evaluation, the urban resource and environmental carrying capacity index is calculated using formulas 15 and 16:

[0145]

[0146] In the formula, RECC p Refers to the potential resource and environmental carrying capacity index, RECC r Refers to the actual resource and environmental carrying capacity index. ES refers to the ecosystem service function index, WSS refers to the soil and water conservation function index, ER refers to the ecosystem sensitivity index, RR refers to the resource endowment restriction index, EQS refers to the environmental quality safety index, CR refers to the urban development resilience index, IP refers to the infrastructure perfection index, and SD refers to the social development intensity index; the larger the resource and environmental carrying capacity index, the stronger the carrying capacity of the resources and environment.

[0147] Among the above methods, the following methods can be used to analyze the spatial characteristics and visualize the urban resource and environmental carrying capacity:

[0148] (1) In each evaluation basic unit, the global Moran index of the resource and environmental carrying capacity index was calculated using ArcMap 10.8 software, and the spatial aggregation characteristics of the resource and environmental carrying capacity were analyzed using the hot spot analysis method. The calculation method is shown in Formula 17 and Formula 18:

[0149]

[0150] In the formula, n refers to the number of resource and environmental carrying capacity assessment units, X i and X j Refers to the resource and environmental carrying capacity index value, w ij Refers to the spatial weight matrix between each evaluation unit, I refers to the global spatial autocorrelation index (Moran's I), and its value range is [-1,1]. When I>0, the spatial correlation is positive, and the closer it is to 1, the more significant the spatial difference is. When I<0, on the contrary, when I=0, there is no spatial correlation. Refers to the aggregation index, when When , it is a hot spot area, otherwise it is a cold spot area, and The closer the value is to 0, the more obvious the clustering is. S refers to the standard deviation.

[0151] (2) With the help of ArcMap10.8 software, the results of the urban resource and environmental carrying capacity evaluation in the study area were displayed on the map in a hierarchical manner according to the "natural breakpoint" classification method, and a comprehensive evaluation result map of the urban resource and environmental carrying capacity in the study area was generated. The Moran index and clustering index of the resource and environmental carrying capacity evaluation results of the study area were spatially mapped using ArcMap10.8 software to obtain the results of the spatial characteristics analysis of the resource and environmental carrying capacity of the study area, and finally a spatial characteristics analysis result map of the urban resource and environmental carrying capacity of the study area was generated.

[0152] Application examples:

[0153] Wuhan City, Hubei Province was selected as a case area, and a comprehensive evaluation of the urban resource and environmental carrying capacity of Wuhan City was conducted based on the method of multi-source spatiotemporal big data integration.

[0154] Step 1: Obtain remote sensing images, online map services, social media information, geospatial information, and statistical data of Wuhan City through online download, open source map service interface call, web crawling, or website application, spatialize and standardize each type of data, and generate a comprehensive multi-source spatiotemporal dataset with unified coordinates, including:

[0155] (1) By accessing the Geospatial Data Cloud and the website of the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, download remote sensing image data including the 2020 land use classification, digital elevation, normalized difference vegetation index, and night lights of Wuhan City, and pre-process various remote sensing image data through three steps: projection transformation, data cropping, and data mosaicking, so that the coordinate projection of various remote sensing image data is the same;

[0156] (2) By writing a Python programming language program to call the online open source map service interface, we obtained the geographical points of interest (POIs), traffic road network, and social media information data containing the terms "flood disaster", "urban waterlogging", and "road waterlogging" in Wuhan, and performed data cleaning and data spatial preprocessing;

[0157] (4) By visiting the websites of the National Earth Science System Data Service Center and the National Meteorological Science Data Center, apply to download geospatial information data including soil organic matter content, precipitation, solar radiation and geological stability in Wuhan, and perform three steps of pre-processing on various geospatial information data, namely, projection transformation, data clipping and data mosaicking, so that their coordinate projections are the same;

[0158] (5) Visit the websites of Wuhan Statistics Bureau, Health Commission and Water Affairs Bureau, apply to download Wuhan Statistical Yearbook, Health Statistics Yearbook and Water Resources Bulletin 2020, and perform data cleaning and spatial preprocessing to obtain four types of statistical data on Wuhan’s population, GDP, water resource utilization rate and sewage treatment rate;

[0159] (6) The above pre-processed data of Wuhan’s land use classification, digital elevation, normalized difference vegetation index, night light remote sensing images, geographic points of interest (POI), traffic network, social media information, soil organic matter content, average annual precipitation, average annual solar radiation, geological stability, population, GDP, water resource utilization rate, and sewage treatment rate were all spatially corrected and pre-processed. Then, the data were clipped according to the shape of the administrative boundaries of Wuhan’s urban area to make the data set consistent with the spatial range of the study area, thus generating a multi-source spatiotemporal data set for the comprehensive evaluation of Wuhan’s urban resource and environmental carrying capacity.

[0160] Step 2: Determine the comprehensive evaluation index system of urban resource and environmental carrying capacity:

[0161] Combined with the characteristics and current status of the components of Wuhan's urban resource subsystem, environmental subsystem, social and economic subsystem, the resource and environmental carrying system is integrated into two dimensions: potential resource and environmental carrying capacity and actual resource and environmental carrying capacity. The analytic hierarchy process is used to construct the urban potential and actual resource and environmental carrying capacity evaluation index systems. Each system consists of a target layer, a criterion layer and an indicator layer, as shown in Table 1.

[0162] Table 1. Comprehensive evaluation index system for urban resource and environmental carrying capacity

[0163]

[0164] Step 3: Calculate the urban resource and environmental carrying capacity index, as shown below:

[0165] 1. The entropy weight method is used to calculate the weights of each indicator layer in the potential resource and environmental carrying capacity and actual resource and environmental carrying capacity evaluation index system:

[0166] (1) Under the same indicator, the proportion of the value of the jth evaluation indicator in the ith evaluation unit to the sum of the values ​​of the indicator in all evaluation units is P ij , as shown in Formula 1:

[0167]

[0168] In the formula, F ij Refers to the value of the jth indicator in the i-th evaluation unit;

[0169] e j Refers to the entropy value of each indicator, and its calculation method is shown in Formula 2:

[0170]

[0171] In the formula, e j Refers to the entropy value of each indicator, k refers to a constant, k = 1 / ln m, m refers to the number of samples, 0 < e j <1; P ij It refers to the proportion of the value of the jth evaluation indicator in the ith evaluation unit to the sum of the values ​​of the indicator in all evaluation units;

[0172] The calculation method of each indicator weight is shown in Formula 3:

[0173]

[0174] In the formula, w j Refers to the calculated weight values ​​of each evaluation index of potential and actual resource and environmental carrying capacity;

[0175] Secondly, the range standardization method is used to standardize each indicator, as shown in Formula 4 and Formula 5:

[0176] (1) For positive indicators, the standardization method is:

[0177] r ij =[v ij -min(v ij )] / [max(v ij )-min(v ij )] Formula 4

[0178] (2) For negatively pointing indicators, the standardization method is:

[0179] r ij =[max(v ij )-v ij ] / [max(vij )-min(v ij )] Formula 5

[0180] In the formula, r ij Refers to the index value after standardization; v ij Refers to the original value of the indicator data; max x ij Refers to the maximum value of this indicator; min x ij Refers to the minimum value of this indicator. After standardization, the data values ​​of all indicators are kept in the range of [0,1].

[0181] 2. Calculate the resource and environmental carrying capacity index according to the following method:

[0182] (1) The calculation method of the criterion layer indicators in the potential resource and environmental carrying capacity evaluation index system is as follows:

[0183] a. The calculation method of ecosystem service function is shown in Formula 6:

[0184] ES=Wr×w1+Ws×w2+Fs×w3 Formula 6

[0185] In the formula, ES refers to the ecosystem service function index; Wr refers to the water conservation capacity, Ws refers to the wind and sand fixation capacity, and Fs refers to the flood storage capacity; w i Refers to the weight value of this indicator.

[0186] b. The calculation method of soil and water conservation function is shown in Formula 7:

[0187] WSS=Ss×w1+So×w2+NDVI×w3 Formula 7

[0188] Where, WSS refers to the soil and water conservation function index, Ss refers to soil water retention, So refers to soil organic matter content, and NDVI refers to vegetation coverage index; i Refers to the weight value of this indicator.

[0189] c. The calculation method of ecosystem sensitivity is shown in Formula 8:

[0190] ER=Se×w1+As×w2 Formula 8

[0191] Where ER refers to the ecosystem sensitivity index; Se refers to soil erosion sensitivity; As refers to acid rain sensitivity; w i Refers to the weight value of this indicator.

[0192] (2) The calculation method of the criterion layer indicators in the actual resource and environmental carrying capacity evaluation index system is as follows:

[0193] d. The calculation method of resource endowment restriction is shown in Formula 9:

[0194] RR=fs / pop×w1+dem×w2+pw×w3 Formula 9

[0195] In the formula, RR refers to the resource endowment restriction index; fs refers to the cultivated land area; pop refers to the population; dem refers to the terrain relief; pw refers to the per capita water resources; w i Refers to the weight value of this indicator.

[0196] e. The calculation method of environmental quality safety is shown in Formula 10:

[0197] EQS=dr×w1+fr×w2+aq×w3 Formula 10

[0198] In the formula, EQS refers to the environmental quality safety index; dr refers to geological stability; fr refers to flood disaster risk, and the flood disaster risk value is obtained through Python language program; aq refers to the annual average PM2.5 concentration; w i Refers to the weight value of the indicator. The flood disaster risk data was obtained by writing a Python language program to crawl news items related to "flood", "waterlogging" and "flood disaster" on the Internet, and the relevant item information was processed through spatial configuration to generate point feature data with coordinate location attribute information. Based on the point data, kernel density analysis and calculation were performed in ArcMap 10.8 software to obtain the flood disaster risk index in each evaluation unit in the study area.

[0199] f. The calculation method of urban development resilience is shown in Formula 11:

[0200] CR = cn / s×w1+ps / gdp×w2+sn / s×w3 Formula 11

[0201] In the formula, CR refers to the urban development resilience index; cn refers to the number of enterprises and institutions, ps refers to the output value of the tertiary industry, gdp refers to the gross national product of the evaluation unit, sn refers to the number of schools and scientific research institutions, and s refers to the area of ​​the evaluation unit; w i Refers to the weight value of the indicator. The enterprise and institution data is obtained by writing Python language programs to call the relevant service API of Amap to obtain the POI data of all enterprises and institutions in the study area, and perform projection transformation and spatial correction processing.

[0202] g. The calculation method of infrastructure completeness is as follows:

[0203] A grid with a size of 2000 meters is set, and the geometric center point of each grid is taken as the demand point, and the location of various infrastructures is taken as the supply point. By writing a Python language program, the Amap path planning service API is called to obtain the shortest travel time between each demand point and its nearest supply point, and the infrastructure perfection index of the research area is calculated according to formula 12 and formula 13:

[0204] Ti=Min(t i1 , t i2 , t i3 ,...t ij ) Formula 12

[0205]

[0206] Where, T i Refers to the shortest travel time of sampling point i, in seconds, t ij It refers to the optimal travel time required from demand point i to one of the target points of a certain type of infrastructure, j refers to the number of a certain type of infrastructure within 10km of the sampling point. IP refers to the infrastructure integrity index, s i It refers to the shortest travel time between the demand point and its nearest supply point, and n refers to the number of sampling points in the evaluation unit.

[0207] The key Python code for calling the Amap route planning service API is as follows:

[0208]

[0209]

[0210] In order to keep consistent with other indicators, after calculating the infrastructure perfection index, the “Area Tabulation” tool of ArcMap 10.8 software was used to convert the grid-scale Wuhan infrastructure perfection index to the administrative division scale. After the conversion was completed, each of the 160 evaluation units in the study area had an infrastructure perfection index value.

[0211] h. The calculation method of social development intensity is shown in Formula 14:

[0212] SD=pop / s×w1+rl×w2 Formula 14

[0213] In the formula, SD refers to the social development intensity index; pop refers to the total population in the evaluation unit; s refers to the area of ​​the evaluation unit; rl refers to the length of the traffic network in the evaluation unit; w i Refers to the weight value of this indicator.

[0214] In order to analyze the resource and environmental carrying capacity evaluation results on a unified scale unit, the “Area Tabulation” tool of ArcMap 10.8 software was used to convert the potential resource and environmental carrying capacity index at the grid scale to the administrative division scale. The street-level administrative division boundary was used as the basic unit for resource and environmental carrying capacity evaluation. The urban resource and environmental carrying capacity index of the study area was calculated using Formula 15 and Formula 16:

[0215]

[0216] In the formula, RECC p Refers to the potential resource and environmental carrying capacity index, RECC r Refers to the actual resource and environmental carrying capacity index; ES refers to ecosystem service function, WSS refers to ecosystem risk, ER refers to habitat quality; RR refers to resource endowment limitation, EQS refers to environmental quality safety, CR refers to urban development resilience, IP refers to infrastructure perfection, and SD refers to social development intensity; the larger the resource and environmental carrying capacity index, the stronger the carrying capacity of resources and environment.

[0217] Through the calculation of formula 1 to formula 16, the comprehensive evaluation results of Wuhan's resource and environmental carrying capacity are obtained. The "natural breakpoint" classification method of ArcMap 10.8 software is used to divide the resource and environmental carrying capacity evaluation results into five categories, namely "high", "relatively high", "medium", "low", and "low". The resource and environmental carrying capacity of each evaluation unit is displayed in a graded manner. The darker the color, the higher the resource and environmental carrying capacity index of the unit. Through the classification threshold division standard shown in Table 2, the thematic map of the evaluation results of urban resource and environmental carrying capacity in the study area is generated as shown in Table 2. Figure 3 shown.

[0218] Table 2: Threshold Standards for Resource and Environmental Carrying Capacity Index

[0219] Potential resource and environmental carrying capacity index range 0.00-0.03 0.03-0.10 0.10-0.20 0.20-0.37 0.37-1.00 Potential resource and environmental carrying capacity Low Lower medium Higher high Real resource and environmental carrying capacity index range 0.00-0.10 0.10-0.16 0.16-0.21 0.21-0.28 0.28-1.00 Current resource and environmental carrying capacity Low Lower medium Higher high

[0220] Step 4: Analyze the spatial distribution characteristics of the urban resource and environmental carrying capacity assessment results and visualize the analysis results, as shown below:

[0221] (1) In each evaluation basic unit, the global Moran's index (Moran's I) of the potential resource and environmental carrying capacity index and the actual resource and environmental carrying capacity index are calculated respectively, and the hot spot analysis method is used to explore the spatial aggregation characteristics of resource and environmental carrying capacity. The calculation method is shown in Formula 17 and Formula 18:

[0222]

[0223] In the formula, n refers to the number of resource and environmental carrying capacity assessment units, n = 160, X i and Xj Refers to the value of the resource and environment carrying capacity index, w ij Refers to the spatial weight matrix between each evaluation unit. I refers to the global spatial autocorrelation index, and its value range is [-1, 1]. When I>0, that is, the spatial correlation is positive, and the closer it is to 1, the more significant the spatial difference. When I<0, the opposite is true. When I = 0, there is no spatial correlation. Refers to the aggregation index. When it is the hot spot area, otherwise it is the cold spot area, and the less the value is close to 0, the more obvious the aggregation, and S refers to the standard deviation.

[0224] (2) Use the ArcMap 10.8 software to perform spatial visualization on the Moran index and aggregation index of the resource and environment carrying capacity evaluation results of Wuhan City, and display the spatial characteristics analysis results of the resource and environment carrying capacity of the research area on the map in grades, which are "extremely hot", "hot", "relatively hot", "relatively cold", "cold", "extremely cold", and "insignificant", a total of seven categories. The stronger the spatial aggregation of the evaluation unit with darker colors, and the generated map of the spatial characteristics analysis results of the urban resource and environment carrying capacity of Wuhan City is as Figure 4 shown.

[0225] The comprehensive evaluation method for urban resource and environment carrying capacity based on multi-source spatio-temporal data integration provided by the present invention has the following characteristics:

[0226] (1) It not only effectively makes up for the deficiencies of traditional data and methods in the accuracy and currency of the comprehensive evaluation of urban resource and environment carrying capacity, but also formulates a technical model for integrating and applying multi-source heterogeneous spatio-temporal data to the analysis in the resource and environment field, greatly improving the scientificity and practicality of the evaluation of urban resource and environment carrying capacity.

[0227] (2) Aiming at the characteristics of the complex composition and variable structure of the urban resource and environment system, starting from two dimensions of potential and reality, it excavates the comprehensive state of the urban resource and environment carrying capacity from a more microscopic perspective, and deeply identifies the comprehensive spatial distribution characteristics of the urban resource and environment carrying capacity, which can provide a scientific and reasonable reference basis and decision-making support for the urban natural resource management department to formulate urban territorial space planning and optimization strategies.

Claims

1. A comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration, characterized by: First, a multi-source spatiotemporal dataset including remote sensing images, open source map services, social media information, geospatial information and statistical data is constructed to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity; then, based on the multi-source spatiotemporal dataset, the potential and actual resource and environmental carrying capacity indexes are calculated using grids and administrative divisions as evaluation units; finally, the spatial characteristics of resource and environmental carrying capacity are analyzed using spatial autocorrelation and hot spot analysis methods, and the resource and environmental carrying capacity evaluation result map and its spatial characteristic distribution map are drawn; The multi-source spatiotemporal dataset is constructed using the following method: (1) Preprocessing of remote sensing image data including land use classification, digital elevation, normalized difference vegetation index, and night light by projection transformation, data clipping, and mosaicking; (2) Write a Python program to call the online open source map service interface to obtain geographic point of interest (POI) data, traffic network data, and social media information data, and perform data cleaning and data spatialization preprocessing; (4) Preprocessing of geospatial information data on soil organic matter content, annual average precipitation, annual average solar radiation, and geological stability by projection transformation, data clipping, and data mosaicking; (5) Preprocessing of data cleaning and data spatialization steps for statistical yearbooks, health statistics yearbooks, and water resources bulletin statistical data; (6) The pre-processed land use classification, digital elevation, normalized difference vegetation index, night light remote sensing images, geographic points of interest (POIs), transportation network, social media entries, soil organic matter content, average annual precipitation, average annual solar radiation, geological stability, population, GDP, water resource utilization rate, and sewage treatment rate data were spatially corrected and then clipped according to the boundary shape of the study area to make the dataset consistent with the spatial range of the study area, thus generating a multi-source spatiotemporal dataset for the comprehensive evaluation of urban resource and environmental carrying capacity in the study area.

2. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The following method is used to determine the comprehensive evaluation index system of urban resource and environmental carrying capacity: Focusing on the characteristics and current status of the components of the urban resource subsystem, environmental subsystem, social and economic subsystem, the resource and environmental carrying system is integrated into two dimensions: potential resource and environmental carrying capacity and actual resource and environmental carrying capacity. The analytic hierarchy process is used to construct the urban potential and actual resource and environmental carrying capacity evaluation index systems. Each system consists of a target layer, a criterion layer and an index layer. The target layer is the potential resource and environmental carrying capacity and the actual resource and environmental carrying capacity. The criteria layer of the potential resource and environmental carrying capacity evaluation index system includes ecosystem service functions, soil and water conservation functions, and ecosystem sensitivity indicators; The criteria layer of the actual resource and environmental carrying capacity evaluation index system includes: resource endowment limitation, environmental quality safety, urban development resilience, infrastructure perfection, and social development intensity indicators.

3. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 2 is characterized in that: The calculation method of the indicator data of each criterion layer in the resource and environmental carrying capacity evaluation index system is as follows: (1) Calculation method of the criterion level indicators in the potential resource and environmental carrying capacity evaluation index system: a. The calculation method of ecosystem service function is shown in Formula 1: ES=Wr×w1+Ws×w2+Fs×w3 Formula 1 In the formula, ES refers to the ecosystem service function index; Wr refers to the water conservation capacity, Ws refers to the wind and sand fixation capacity, and Fs refers to the flood storage capacity; w i Refers to the weight value of the indicator; b. The calculation method of soil and water conservation function is shown in Formula 2: WSS=Ss×w1+So×w2+NDVI×w3Formula 2 Where, WSS refers to the soil and water conservation function index, Ss refers to soil water retention, So refers to soil organic matter content, and NDVI refers to vegetation coverage index; i Refers to the weight value of the indicator; c. The calculation method of ecosystem sensitivity is shown in Formula 3: ER=Se×w1+As×w2 Formula 3 Where ER refers to the ecosystem sensitivity index; Se refers to soil erosion sensitivity; As refers to acid rain sensitivity; w i Refers to the weight value of the indicator; (2) Calculation method of criterion-level indicators in the current resource and environmental carrying capacity evaluation index system: d. The calculation method of resource endowment restriction is shown in Formula 4: RR=fs / pop×w1+dem×w2+pw×w3 Formula 4 In the formula, RR refers to the resource endowment restriction index; fs refers to the cultivated land area; pop refers to the population; dem refers to terrain relief, pw refers to per capita water resources, w i Refers to the weight value of the indicator; e. The calculation method of environmental quality safety is shown in Formula 5: EQS=dr×w1+fr×w2+aq×w3 Formula 5 In the formula, EQS refers to the environmental quality safety index; dr refers to geological stability; fr refers to flood risk, and the flood risk value is obtained through Python language program; aq refers to the annual average PM2.5 concentration; w i Refers to the weight value of the indicator; f. The calculation method of urban development resilience is shown in Formula 6: CR=cn / s×w1+ps / gdp×w2+sn / s×w3Formula 6 In the formula, CR refers to the urban development resilience index; cn refers to the number of enterprises and institutions, ps refers to the output value of the tertiary industry, gdp refers to the gross national product of the evaluation unit, sn refers to the number of schools and scientific research institutions, and s refers to the area of ​​the evaluation unit; w i Refers to the weight value of the indicator; g. The calculation method of infrastructure completeness is as follows: Set a grid size of 2000 meters, take the geometric center point of each grid as the demand point, and the location of various infrastructure as the supply point. By writing a Python language program, call the Amap route planning service API to obtain the shortest travel time between each demand point and its nearest supply point. The specific calculation method is shown in Formula 7 and Formula 8: T i =Min(t iI , t i2 , t i3 ,...t ij ) Formula 7 in, Where, T i Refers to the shortest travel time of sampling point i, in seconds, t ij It refers to the optimal travel time required from demand point i to one of the target points of a certain type of infrastructure; j refers to the number of a certain type of infrastructure within 10 km of the sampling point; IP refers to the infrastructure completeness index, s i It refers to the shortest travel time between the demand point and its nearest supply point, and n refers to the number of sampling points in the evaluation unit. To keep consistent with other indicators, after calculating the infrastructure perfection index, the grid-scale infrastructure perfection index is converted to the administrative division scale through the area averaging method. h. The calculation method of social development intensity is shown in Formula 9: SD=pop / s×w1+rl×w2 Formula 9 In the formula, SD refers to the social development intensity index; pop refers to the total population in the evaluation unit; s refers to the area of ​​the evaluation unit; rl refers to the length of the traffic network in the evaluation unit; w i Refers to the weight value of this indicator.

4. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The entropy weight method is used to calculate the weight of each evaluation index. The specific steps are as follows: (1) Under the same indicator, the proportion of the value of the jth evaluation indicator in the i-th evaluation unit to the sum of the values ​​of the indicator in all evaluation units is P ij , expressed by formula 10: In the formula, F ij Refers to the value of the jth indicator in the i-th evaluation unit; e j Refers to the entropy value of each indicator, calculated using formula 11 and formula 12: In the formula, k is a constant, k = l / ln m, m is the number of samples, 0 < e j <1; The weight of each evaluation index is calculated using formula 12: In the formula, w j Refers to the weight value of each indicator.

5. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The range standardization method is used to standardize each indicator, and the formula 13 and formula 14 are used to calculate: (1) For positive indicators, the standardization method is: r ij = [v ij - min(v ij )] / [max(v ij ) - min(v ij )] Equation 13 (2) For negatively pointing indicators, the standardization method is: r ij = [max(v ij ) - v ij / [max(v ij ) - min(v ij )] Equation 14 In the formula, r ij Refers to the index value after standardization; v ij Refers to the original value of the indicator data; max(v ij ) refers to the maximum value of the indicator; min(v ij ) means the minimum value of that indicator.

6. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The grid is used as the basic unit for evaluating the potential resource and environmental carrying capacity, and the administrative division boundary is used as the basic unit for evaluating the actual resource and environmental carrying capacity; The urban resource and environmental carrying capacity index is comprehensively calculated using Formula 15 and Formula 16: In the formula, RECC p Refers to the potential resource and environmental carrying capacity index, RECC r It refers to the actual resource and environmental carrying capacity index, ES refers to ecosystem service function, WSS refers to ecosystem risk, ER refers to habitat quality; RR refers to resource endowment limitation, EQS refers to environmental quality safety, CR refers to urban development resilience, IP refers to infrastructure perfection, and SD refers to social development intensity.

7. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The spatial distribution characteristics of urban resource and environmental carrying capacity are calculated using the following method: (1) In each basic evaluation unit, the global Moran index of the actual resource and environmental carrying capacity index is calculated using Formula 17: In the formula, I refers to the global Moran's I, n refers to the number of resource and environmental carrying capacity assessment units, and X i and X j Refers to the resource and environmental carrying capacity index value, w ij Refers to the spatial weight matrix between each evaluation unit; Using the hot spot analysis method, the spatial aggregation characteristics of the resource and environmental carrying capacity index of the study area were calculated using Formula 18: in, In the formula, refers to the clustering index, and S refers to the standard deviation; (2) The Moran index and clustering index of the potential and actual resource and environmental carrying capacity index of the study area were spatially mapped to obtain the results of the spatial characteristics analysis of the resource and environmental carrying capacity of the study area.

8. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to claim 1 is characterized in that: The following method is used to visualize the resource and environmental carrying capacity assessment results: based on the natural breakpoint method, the urban resource and environmental carrying capacity assessment results and the spatial characteristic analysis results of the resource and environmental carrying capacity in the study area are displayed in a hierarchical manner on the map, and a comprehensive assessment result map of the urban resource and environmental carrying capacity in the study area and a spatial characteristic analysis result map are generated respectively.

9. The comprehensive evaluation method for urban resource and environmental carrying capacity based on multi-source spatiotemporal data integration according to any one of claims 1 to 8, characterized in that: Used to deeply explore the comprehensive status of urban resource and environmental carrying capacity.

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